Data Driven Inverse Design

Abstract:

Artificial intelligence and specially machine learning have revolutionized visual computing and natural language processing. However, they have had a limited success when it comes to manufacturing, an equally important domain where we design, engineer and fabricate physical products. This is partly due to our reliance on off-the-shelf tools in this domain, rather than developing original AI algorithms. In this talk, I will show some examples of deep integration of AI and manufacturing. I will focus on inverse design, a paradigm of content synthesis in engineering design and digital fabrication. Specifically, I will discuss a class of inverse design problems that deals with data-driven neural surrogate models. These surrogates learn and replace a forward process, such as a computationally heavy simulation. I will show that by delving deeper into the properties of neural surrogate models, such as their piecewise-linearity or their ability to compute predictive uncertainty, we can develop powerful data driven inverse design pipelines.

Biography:

Vahid Babaei leads the AI-aided Design and Manufacturing group at the Max Planck Institute for Informatics in Saarbrücken, Germany. He was a postdoctoral researcher at the Computational Design and Fabrication Group of Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT. He obtained his PhD in Computer Science from EPFL. Vahid Babaei is the recipient of the 2023 Germany-wide Curious Mind Award in the area of ‘AI, Digitalization, and Robotics’, the Hermann Neuhaus Prize of the Max Planck Society, and two postdoctoral fellowships awarded by the Swiss National Science Foundation. Vahid Babaei’s research interests lie in leveraging computational methods for both engineering design and advanced manufacturing, and putting these methods into practice in order to create physical products with novel and useful properties.